Contract number: 65A0686

Funding amount: $100,000

Performance period: 3/1/2020 to 12/31/2021

Link to final report: https://escholarship.org/uc/item/8rw99523

Link to research brief: https://escholarship.org/uc/item/4xj5w07g

Project description

Vehicle emissions have a significant impact on the environment, and energy consumption is directly related to emissions. Medium and heavy-duty trucks were responsible for more than 6% of the U.S.A.'s emissions in the year of 2016. The problems of energy management and eco-routing have been studied before. However, when it comes to the trucking industry these studies do not consider other important practical factors, like working hours regulations and parking availability. An eco-route for example that does not take into account parking availability at the time that is needed may lead to changes that makes the route more polluting than the one planned. Similarly working hours regulations may influence the parking rest area timing and that may involve deviations from an initially selected eco-route with negative consequences on the environment and fuel consumption. 

The purpose of this project is to integrate solutions for eco-routing and practical constraints, and develop a more practical model for eco-routing in the trucking industry, paving the way to more complex energy and emissions optimization methods, like the organization of truck platoons, which will need a more realistic underlying model in order to be implemented. As a first step, a model for the Vehicle Shortest Path and Truck Driver Scheduling Problem with Parking Availability developed under a different project will be extended in order to include the impact of traffic conditions on travel times used in the problem. This first step brings the model to a more realistic level, as one of the drawbacks of the previous model was the lack of time-dependency in the travel-times, which is not true in practice. The second step is to turn the truck speed or travel time into a decision variable, which will allow the algorithm to exploit the effects of the vehicle speed on fuel consumption and emissions. The third step is to choose an appropriate emission model to be included in the optimization model and used to estimate the environmental cost of the trip. We will choose an appropriate cost function for the optimization problem, which will depend on the relative importance given by the user to the environmental impact and other factors. In addition, we will extend the model to account for the uncertainty in parking availability. Some realistic scenarios from Southern California will be used to evaluate the solution of the optimization problem and quantify the possible benefits. We plan to examine the feasibility of commercialization of the proposed truck routing methodology that takes into account the demand, dynamic travel times, parking availability with uncertainties, regulations as well as fuel consumption and emissions. Trade-offs will be identified and the user will be given the flexibility to adjust these trade-offs by using different weights in the optimization formulation. The methodology will be applicable in today's environment will be flexible to include advances in sensing and information technologies.